An erosion gully classification method and device, electronic equipment and storage medium

Abstract

The application discloses an erosion gully classification method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring satellite images of a research area, and performing geographical coordinate calibration on the satellite images; acquiring a digital elevation model of the research area based on the calibrated satellite images, and performing pretreatment on the digital elevation model, and generating a contour map according to the pretreated digital elevation model; dividing the research area into a plurality of small watersheds based on the contour map; performing visual interpretation operation on each small watershed, and performing erosion gully classification processing on the research area based on each small watershed after the visual interpretation operation and a preset classification criterion. The application solves the technical problem that the current erosion gully classification method cannot scientifically classify erosion gullies according to the development topography of the erosion gullies.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water and soil conservation, and particularly relates to an erosion gully classification method and device, an electronic device and a storage medium. BACKGROUND

[0002] Black soil is a valuable natural resource, however, the topography of typical black soil area is long and gentle, and the concentrated rainfall in summer easily causes soil erosion. At present, gully erosion in typical black soil area is very serious. Gully erosion is a manifestation of serious land degradation, and the development of erosion gully seriously damages land productivity and poses a threat to people's life and property. Therefore, the research on gully erosion has attracted much attention from scholars.

[0003] In order to study erosion gully, a scientific erosion gully classification method is very important. At present, there is no uniform standard for the classification of erosion gully, and different researchers have different classifications of erosion gully in their research, including classification by morphological characteristics of erosion gully or classification by development position of erosion gully, but none of them can scientifically classify erosion gully according to the development topography of erosion gully. SUMMARY

[0004] The present application aims to overcome the above technical deficiencies and provide an erosion gully classification method, device, electronic device and storage medium, which solves the technical problem that the existing erosion gully classification method cannot scientifically classify erosion gully according to the development topography of erosion gully.

[0005] To achieve the above technical purpose, the present application adopts the following technical solution:

[0006] In a first aspect, the present application provides an erosion gully classification method, comprising the following steps:

[0007] Obtaining satellite images of a study area and calibrating the geographic coordinates of the satellite images;

[0008] Obtaining a digital elevation model of the study area based on the calibrated satellite images, and generating a contour map according to the pre-processed digital elevation model;

[0009] Dividing the study area into several small watersheds based on the contour map;

[0010] Performing visual interpretation operation on each small watershed, and classifying the erosion gully of the study area based on each small watershed after the visual interpretation operation and a preset classification criterion.

[0011] In some embodiments, the method of geographic coordinate calibration is:

[0012] The satellite image is equally divided in vertical and horizontal directions to obtain nine regions, and then a plurality of unchanged landmark features in each region are selected as registration points, and each region is registered by using a preset geographic registration tool.

[0013] In some embodiments, a digital elevation model of the study area is obtained based on the calibrated satellite image, and the digital elevation model is preprocessed, and then a contour map is generated based on the preprocessed digital elevation model, including:

[0014] A digital elevation model of the study area is obtained based on the calibrated satellite image.

[0015] After obtaining a preset equal interval, the digital elevation model is processed by using a preset surface analysis tool to obtain a contour map.

[0016] In some embodiments, the study area is divided into a plurality of small watersheds based on the contour map, including:

[0017] The contour map is divided by a watershed division based on a preset division constraint condition to obtain a plurality of small watersheds, wherein the connection line of each small watershed is a watershed line of the watershed, and the watershed line is a connection line of the highest point of the watershed.

[0018] In some embodiments, the preset division constraint condition is:

[0019] The precipitation in the small watershed converges to the same valley, and each small watershed has only one valley bottom ditch as a water flow outlet.

[0020] In some embodiments, the erosion gully classification processing of the study area is performed based on the visual interpretation of each small watershed and a preset classification criterion, including:

[0021] Each small watershed is visually interpreted to delineate an erosion gully with a clear gully edge line in each small watershed, wherein each erosion gully has a gully head and a gully mouth.

[0022] Data of each erosion gully is obtained and saved, wherein the data of the erosion gully at least includes a gully length, a maximum width of the erosion gully, a minimum width of the erosion gully, an average width of the erosion gully, and an area of the erosion gully.

[0023] Each erosion gully is classified based on the data of each erosion gully and a preset classification criterion.

[0024] In some embodiments, the preset classification criterion is:

[0025] when the erosion gully exceeds 80% of the angle between the center line of the gully and the contour line, and the slope of the slope surface perpendicular to the center line of the gully is greater than the slope of the center line of the gully, the erosion gully is divided into a slope gully.

[0026] when the erosion gully exceeds 80% of the angle between the center line of the gully and the contour line, and the slope of the slope surface perpendicular to the center line of the gully is greater than the slope of the center line of the gully, the erosion gully is divided into a slope gully.

[0027] In a second aspect, the present application further provides an erosion gully classification device, comprising:

[0028] a satellite image acquisition module, configured to acquire satellite images of a study area, and to calibrate geographical coordinates of the satellite images;

[0029] a contour map generation module, configured to acquire a digital elevation model of the study area based on the calibrated satellite images, to pre-process the digital elevation model, and to generate a contour map based on the pre-processed digital elevation model;

[0030] a division module, configured to divide the study area into a plurality of small watersheds based on the contour map;

[0031] a classification module, configured to perform visual interpretation on each of the small watersheds, and to perform erosion gully classification processing on the study area based on each of the small watersheds after the visual interpretation and a preset classification criterion.

[0032] In a third aspect, the present application further provides an electronic device, comprising a processor and a memory;

[0033] the memory stores a computer program which can be executed by the processor;

[0034] the processor executes the computer program to implement the steps in the erosion gully classification method.

[0035] In a fourth aspect, the present application further provides a computer readable storage medium, which stores one or more programs which can be executed by one or more processors to implement the steps in the erosion gully classification method.

[0036] Compared with the prior art, the erosion gully classification method, device, electronic equipment and storage medium provided by the present application first acquire satellite images of a study area, calibrate the geographic coordinates of the satellite images, then acquire a digital elevation model of the study area based on the calibrated satellite images, and after preprocessing the digital elevation model, generate a contour map according to the preprocessed digital elevation model, then divide the study area into a plurality of small watersheds based on the contour map, and finally perform visual interpretation on each small watershed, and based on each small watershed after the visual interpretation and a preset classification criterion, perform erosion gully classification processing on the study area. The present application classifies erosion gullies scientifically by different dominant erosion gully development factors, provides scientific basis and suggestions for human management of the occurrence and development of erosion gullies, and after classification, different influencing gully development factors are selected for different gullies, and linear regression analysis can improve R 2 , which provides a theoretical basis for finding factors affecting gullies of different development degrees. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 is a flowchart of the erosion gully classification method provided by the embodiment of the present application;

[0038] Figure 2 is a visual interpretation diagram in the erosion gully classification method provided by the embodiment of the present application;

[0039] Figure 3 is a gully classification diagram in the erosion gully classification method provided by the embodiment of the present application;

[0040] Figure 4 is a functional module diagram of the erosion gully classification device provided by the embodiment of the present application;

[0041] Figure 5 is a hardware structure diagram of the electronic equipment provided by the embodiment of the present application. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0043] Please refer to Figure 1 , the present application provides an erosion gully classification method, comprising the following steps:

[0044] S100, acquiring satellite images of a study area, and calibrating the geographic coordinates of the satellite images;

[0045] S200, acquire a digital elevation model of the research area based on the calibrated satellite image, and generate a contour map according to the pretreated digital elevation model;

[0046] S300, divide the research area into a plurality of small watersheds based on the contour map;

[0047] S400, perform visual interpretation operation on each small watershed, and perform erosion gully classification processing on the research area based on each small watershed after the visual interpretation operation and a preset classification criterion.

[0048] In this embodiment, first, the satellite image of the research area is acquired, and the satellite image is calibrated in geographical coordinates, then the digital elevation model of the research area is acquired based on the calibrated satellite image, and the contour map is generated according to the pretreated digital elevation model, then the research area is divided into a plurality of small watersheds based on the contour map, and finally the visual interpretation operation is performed on each small watershed, and the erosion gully classification processing is performed on the research area based on each small watershed after the visual interpretation operation and a preset classification criterion. The present application classifies the erosion gully scientifically by different dominant erosion gully development factors, provides scientific basis and suggestions for human management of the occurrence and development of erosion gully, and after classification, different influence gully development factors are selected for different gullies, and linear regression analysis can improve R 2 , and provides a theoretical basis for finding factors affecting gullies of different development degrees.

[0049] In some embodiments, in step S100, first, the satellite image of the research area is acquired, for example, the satellite images of the United States CORONA satellite on May 28, 1970 and August 6, 1970 can be acquired, with a resolution of 2.5m, or the satellite images of the French Pleiades satellite in 2010 and 2021, with a resolution of 0.7m, the present application does not limit this, and the selection of the research area can be selected according to the actual situation, for example, the research area can be selected as Nenjiang City (125°6'56"E~125°27'0"E, 48°43'00"N~48°52'48"N) in Heilongjiang Province, Hailun City (126°52'46"E~127°10'22"E, 47°15'59"N~47°32'53"N) in Heilongjiang Province, or Harbin City (126°55'12"E~127°24'0"E, 46°22'12"N~46°26'24"N) in Heilongjiang Province.

[0050] After the satellite image is acquired, in order to facilitate subsequent use, the satellite image needs to be processed for geographic coordinate calibration. Specifically, the method for geographic coordinate calibration is:

[0051] The satellite image is equally divided into three parts in the vertical direction and the horizontal direction to obtain nine regions. After selecting a plurality of unchanged landmark features as registration points in each region, a preset geographic registration tool is used to register each region.

[0052] In this embodiment, geographic coordinate registration can be quickly realized by ArcGIS. The ArcGIS product line provides a scalable and comprehensive GIS platform for users. For example, the satellite image map is imported into ARCGIS, the spatial reference of the two-year image is selected as WGS_1984_UTM_zone_51N, and the spatial calibration is performed in ARCGIS. The "geographic registration" tool in ARCGIS is used to register the two-year image, the datum is selected as 2014, and specifically, the image is equally divided into three parts in the vertical direction and the horizontal direction to divide the image into nine regions. Four to five registration points are selected in each region of the image. The unchanged features in the town of the satellite image are selected for registration. When there is no town in a certain region, the perennial landmark features such as roads and rivers are selected for registration.

[0053] In some embodiments, in step S200, the digital elevation model (DEM) is realized by limited terrain elevation data to digitally simulate the terrain (i.e., digital expression of the terrain surface form), which is a solid ground model that represents the ground elevation in the form of an ordered numerical array. It is a branch of digital terrain model (Digital Terrain Model, DTM), and other terrain feature values can be derived from it. Specifically, DEM is a zero-order simplex single digital landform model, and other landform characteristics such as slope, slope direction, and slope change rate can be derived based on DEM.

[0054] In this embodiment, a digital elevation model is first obtained according to the satellite image. The data resolution of the digital elevation model is 6.6 m, which is relatively easy to obtain and can meet the accuracy requirements.

[0055] Optionally, the step S200 specifically includes:

[0056] Based on the calibrated satellite image, a digital elevation model of the research area is obtained;

[0057] After obtaining the preset equal interval, a preset surface analysis tool is used to process the digital elevation model to obtain a contour map.

[0058] In this embodiment, the obtained DEM elevation model is imported into ARCGIS. By importing the obtained DEM elevation model into ARCGIS, the DEM data file is imported through ArcToolbox→Spatial Analyst (spatial analysis tool, used to select contour lines in surface analysis)→surface analysis tool (surface analysis is a built-in tool of ARCGIS, and the contour lines of DEM data can be drawn through the imported DEM file to obtain the contour lines). The equal interval is set to 3m, and ARCGIS automatically processes to export the 3m contour lines, that is, the contour line map corresponding to the DEM is generated.

[0059] In some embodiments, the step S300 specifically comprises:

[0060] Based on the preset division constraint condition, the contour line map is subjected to watershed division to obtain a plurality of small watersheds, wherein the connecting line of each small watershed is the watershed line of the watershed, and the watershed line is the connecting line of the highest point of the watershed.

[0061] In this embodiment, the watershed is divided according to the contour line map, and a plurality of small watersheds are obtained. Specifically, the ridge line of the watershed is called the watershed line, which is generally the connecting line of the highest point of the watershed. The watershed line is connected, which is the boundary line of adjacent watersheds. In this way, the study area is divided into a plurality of small watersheds. It should be noted that the small watershed division needs to meet the division constraint condition. Specifically, the preset division constraint condition is that the precipitation in the small watershed will converge to the same valley, and each small watershed has only one valley bottom as the water flow outlet. When the small watershed exceeds one valley, the small watershed division range needs to be reduced.

[0062] In some embodiments, the step S400 specifically comprises:

[0063] Each small watershed is subjected to visual interpretation operation to draw clear erosion gullies in each small watershed, wherein each erosion gully has a gully head and a gully mouth;

[0064] Data of each erosion gully is obtained and saved, wherein the data of the erosion gully at least includes gully length, maximum width of the erosion gully, minimum width of the erosion gully, average width of the erosion gully, and area of the erosion gully;

[0065] Based on the data of each erosion gully and the preset classification criterion, each erosion gully is classified.

[0066] In this embodiment, a new personal geodatabase is created in the "Window" → "Directory" section of the ArcGIS interface. Line feature classes and polygon feature classes are created within the geodatabase. The "Edit" function in ArcGIS is used to edit the line feature classes and polygon feature classes for visual interpretation of the gullies. Visual interpretation only requires drawing the shape of the gully along its edge in the image. During visual interpretation, the gully size must not be lower than the resolution. Only erosion gullies with clearly defined edges are drawn; narrow and shallow gullies are not drawn. Each gully must have a head and an opening. When drawing complex gully systems, starting from the outlet, the gully with the largest angle between each part and the contour line is drawn first and designated as the main gully. Other gullies are drawn from their heads to the parts connected to the main gully. Figure 2 As shown, it is a channel diagram formed after visual interpretation of a specific embodiment.

[0067] After visual interpretation is completed and each erosion gully is delineated, the length, maximum and minimum width, average width and area of ​​each gully are measured in ArcGIS and entered into an Excel spreadsheet. Based on the erosion gully data and preset classification criteria, the erosion gullies are then classified.

[0068] In some embodiments, the preset classification criteria are:

[0069] When more than 80% of the erosion gullies have an angle between the centerline of the gully centerline and the contour line that is less than a first preset value, and the slope of the slope perpendicular to the gully centerline is greater than the slope of the gully centerline, the erosion gully is divided into a valley gully; wherein, the valley gully is an erosion gully that is formed by two or more gentle hills at the bottom of the valley.

[0070] When more than 80% of the erosion gullies have an angle between the centerline of the gully centerline and the contour line greater than a second preset value, and the slope of the slope perpendicular to the gully centerline is less than the slope of the gully centerline, the erosion gully is classified as a hillslope gully; wherein, the hillslope gully is an erosion gully developed on the gentle slope of farmland.

[0071] This classification criterion allows for the scientific classification of erosion gullies based on their development rate, such as... Figure 3 The diagram shown illustrates the classification of erosion gullies in a specific embodiment. Furthermore, by calculating the changes in the development rate (mainly area) of erosion gullies over two years and conducting statistical analysis on the development rate of valley floor gullies and slope gullies, it can be concluded that there is a significant difference in their development rates.

[0072] The technical scheme provided by the present application firstly acquires satellite images of a research area, and calibrates geographical coordinates of the satellite images, then acquires a digital elevation model of the research area based on the calibrated satellite images, and generates a contour map according to the digital elevation model after preprocessing the digital elevation model, then divides the research area into a plurality of small watersheds based on the contour map, and finally performs visual interpretation operation on each small watershed, and performs erosion gully classification processing on the research area based on each small watershed after the visual interpretation operation and a preset classification criterion. 2 The present application provides a scientific basis and suggestions for human management of the occurrence and development of erosion gully by scientifically classifying erosion gully through different dominant erosion gully development factors, and can improve R

[0073] Another embodiment of the present application provides an erosion gully classification device, please refer to Figure 4 The erosion gully classification device comprises a satellite image acquisition module 11, a contour map generation module 12, a division module 13 and a classification module 14.

[0074] The satellite image acquisition module 11 is used to acquire satellite images of a research area, and calibrate geographical coordinates of the satellite images.

[0075] The contour map generation module 12 is used to acquire a digital elevation model of the research area based on the calibrated satellite images, and generate a contour map according to the digital elevation model after preprocessing the digital elevation model.

[0076] The division module 13 is used to divide the research area into a plurality of small watersheds based on the contour map.

[0077] The classification module 14 is used to perform visual interpretation operation on each small watershed, and perform erosion gully classification processing on the research area based on each small watershed after the visual interpretation operation and a preset classification criterion.

[0078] In the embodiment, firstly, satellite images of a research area are acquired, and geographical coordinate calibration is performed on the satellite images, then a digital elevation model of the research area is acquired based on the calibrated satellite images, and the digital elevation model is preprocessed, and contour lines are generated according to the preprocessed digital elevation model, then the research area is divided into several small watersheds based on the contour lines, and finally, visual interpretation is performed on each small watershed, and the research area is classified and processed according to the erosion gully based on each small watershed after the visual interpretation and a preset classification criterion. The present application classifies the erosion gully scientifically by different development factors of different dominant erosion gully development, provides scientific basis and suggestions for human management of the occurrence and development of the erosion gully, and after classification, different development factors of different gullies are selected, and linear regression analysis can improve R 2 , which provides a theoretical basis for finding factors affecting gullies of different development degrees.

[0079] It should be noted that the module referred to in the present application refers to a series of computer program instruction segments capable of completing a specific function, and is more suitable for describing the execution process of erosion gully classification than a program. The specific implementation of each module is described in the above method embodiment, which will not be described here.

[0080] In some embodiments, the method for geographical coordinate calibration is:

[0081] The satellite images are divided into nine regions in the vertical and horizontal directions, a plurality of unchanged landmark features are selected as registration points in each region, and a preset geographical registration tool is used to register each region.

[0082] In some embodiments, the contour line generation module is specifically used for:

[0083] A digital elevation model of the research area is acquired based on the calibrated satellite images.

[0084] After a preset equal interval is acquired, a preset surface analysis tool is used to process the digital elevation model to obtain a contour line map.

[0085] In some embodiments, the division module 13 is specifically used for:

[0086] Based on a preset division constraint condition, the contour line map is divided by a watershed to obtain several small watersheds, wherein the connection line of each small watershed is a watershed line of the watershed, and the watershed line is a connection line of the highest point of the watershed.

[0087] In some embodiments, the preset division constraint condition is:

[0088] The precipitation in the small watershed converges to the same valley, and each small watershed has only one valley bottom ditch as a water flow outlet.

[0089] In some embodiments, the classification module 14 is specifically configured to:

[0090] perform visual interpretation on each small watershed to delineate an erosion gully with clear gully rim in each small watershed, wherein each erosion gully has a gully head and a gully mouth;

[0091] obtain data of each erosion gully and save, wherein the data of the erosion gully at least includes gully length, maximum width of the erosion gully, minimum width of the erosion gully, average width of the erosion gully, and area of the erosion gully;

[0092] classify each erosion gully based on the data of each erosion gully and a preset classification criterion.

[0093] In some embodiments, the preset classification criterion is:

[0094] when the erosion gully exceeds 80% of the angle between the center line of the gully center line and the contour line, and the slope of the slope surface perpendicular to the gully center line is greater than the slope of the gully center line, the erosion gully is divided into a valley bottom ditch;

[0095] when the erosion gully exceeds 80% of the angle between the center line of the gully center line and the contour line, and the slope of the slope surface perpendicular to the gully center line is less than the slope of the gully center line, the erosion gully is divided into a slope ditch.

[0096] Another embodiment of the present application provides an electronic device, such as Figure 5 As shown in the figure, the electronic device 10 includes:

[0097] one or more processors 110 and memories 120, Figure 5 In an example, the processor 110 and the memory 120 can be connected through a bus or other means, Figure 5 In an example, the connection through the bus is taken as an example.

[0098] The processor 110 is configured to implement various control logic of the electronic device 10, and can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a single-core microprocessor, an ARM (Acorn RISC Machine), or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof. In addition, the processor 110 can also be any conventional processor, microprocessor, or state machine. The processor 110 can also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, and / or any other such configuration.

[0099] The memory 120 is a non-volatile computer-readable storage medium configured to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as program instructions corresponding to the erosion groove classification method in the embodiments of the present application. The processor 110 executes various functional applications and data processing of the electronic device 10 by running the non-volatile software programs, instructions, and units stored in the memory 120, i.e., implements the erosion groove classification method in the above method embodiments.

[0100] The memory 120 can include a program storage area and a data storage area, wherein the program storage area can store an operating platform and application programs required by at least one function; and the data storage area can store data created according to the use of the electronic device 10, etc. In addition, the memory 120 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state memory device. In some embodiments, the memory 120 can optionally include a memory remotely disposed relative to the processor 110, and these remote memories can be connected to the electronic device 10 through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0101] One or more units are stored in the memory 120 and executed by the one or more processors 110 to perform the erosion groove classification method in any of the above method embodiments, for example, to perform the method steps S100 to S400 in the above described Figure 1

[0102] Another embodiment of the present application provides a computer-readable storage medium storing computer-executable instructions, which are executed by one or more processors, for example, to perform the method steps S100 to S400 in the above described Figure 1

[0103] ​​By way of example, computer readable storage media can include read-only memory (ROM); programmable ROM (PROM); electrically programmable ROM (EPROM); electrically erasable ROM (EEPROM); or flash memory. Volatile memory can include random access memory (RAM), which acts as external cache memory. By way of example, and not limitation, RAM can be provided in numerous forms such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM). Combinations of the above can be used. The disclosed memory components of the operating environments described herein are intended to include one or any other suitable types of memory.

[0104] To sum up, the erosion gully classification method, device, electronic equipment and storage medium of the present application first acquire the satellite image of the study area, and calibrate the geographic coordinates of the satellite image, then acquire the digital elevation model of the study area based on the calibrated satellite image, and preprocess the digital elevation model, generate the contour map according to the preprocessed digital elevation model, then divide the study area into several small watersheds based on the contour map, finally perform visual interpretation operation on each small watershed, and based on each small watershed after the visual interpretation operation and the preset classification criteria, perform erosion gully classification processing on the study area. The present application classifies the erosion gullies scientifically by different dominant erosion gully development factors, provides scientific basis and suggestions for human management of the occurrence and development of erosion gullies, and after classification, different influencing gully development factors are selected for different gullies, and after linear regression analysis, the R 2 , and provides a theoretical basis for finding factors affecting gullies of different development degrees.

[0105] The specific embodiments of the present application described above do not constitute a limitation on the scope of protection of the present application. Any various other corresponding changes and modifications made according to the technical concept of the present application shall be included in the scope of protection of the claims of the present application.

Claims

1. A method of classifying erosion gullies, characterized by, The method comprises the following steps: Satellite images of a study area are acquired and georeferenced; A digital elevation model of the study area is obtained based on the georeferenced satellite images, and contour lines are generated based on the preprocessed digital elevation model; The study area is divided into several small watersheds based on the contour lines, including: The contour lines are divided into several small watersheds based on the preset division constraint condition, wherein the connection line of each small watershed is the watershed line of the watershed, and the watershed line is the connection line of the highest point of the watershed; Each small watershed is visually interpreted, and the study area is classified and processed based on the visually interpreted small watersheds and the preset classification criteria, including: Each small watershed is visually interpreted to draw the clear erosion gully in each small watershed, wherein each erosion gully has a gully head and a gully mouth; Data of each erosion gully are acquired and saved, wherein the data of each erosion gully at least include gully length, maximum width of the erosion gully, minimum width of the erosion gully, average width of the erosion gully, and area of the erosion gully; Each erosion gully is classified based on the data of each erosion gully and the preset classification criteria, wherein the preset classification criteria are: When the included angle between the center line of the gully and the contour line of the gully is less than a first preset value, and the slope of the gully is greater than the slope of the gully center line, the gully is classified as a valley bottom gully; When the included angle between the center line of the gully and the contour line of the gully is greater than a second preset value, and the slope of the gully is less than the slope of the gully center line, the gully is classified as a slope gully.

2. The method of ravin classification according to claim 1, wherein, The method of georeferencing comprises the following steps: The satellite images are equally divided into nine areas in the vertical and horizontal directions, and several unchanged landmark features are selected as registration points in each area, and then the areas are registered by using a preset geographic registration tool.

3. The method of ravin classification according to claim 1, wherein, The method of obtaining the digital elevation model of the study area based on the georeferenced satellite images, and generating the contour lines based on the preprocessed digital elevation model, comprises the following steps: The digital elevation model of the study area is obtained based on the georeferenced satellite images; After obtaining the preset equal interval, the digital elevation model is processed by using a preset surface analysis tool to obtain the contour lines.

4. The method of ravin classification of claim 1, wherein, The preset division constraint condition is that: The precipitation in each small watershed converges to the same gully, and each small watershed has only one valley bottom gully as the water outlet.

5. An erosion gully classification device characterized by, The method comprises the following steps: A satellite image acquisition module is configured to acquire satellite images of a study area and georeference the satellite images; A contour line generation module is configured to obtain a digital elevation model of the study area based on the georeferenced satellite images, and generate contour lines based on the preprocessed digital elevation model; The dividing module is configured to divide the study area into a plurality of small watersheds based on the contour map, and the method comprises the following steps of: performing watershed division on the contour map based on a preset division constraint condition to obtain a plurality of small watersheds, wherein a contour line of each small watershed is a watershed line of the watershed, and the watershed line is a connecting line of the highest point of the watershed; The classification module is configured to perform visual interpretation on each small watershed, and perform erosion gully classification processing on the study area based on each small watershed after the visual interpretation and a preset classification criterion; the method comprises the following steps of: performing visual interpretation on each small watershed to delineate an erosion gully with a clear gully rim in each small watershed, wherein each erosion gully has a gully head and a gully mouth; obtaining data of each erosion gully and saving the data, wherein the data of the erosion gully at least includes a gully length, a maximum width of the erosion gully, a minimum width of the erosion gully, an average width of the erosion gully, and an area of the erosion gully; classifying each erosion gully based on the data of each erosion gully and a preset classification criterion; and the preset classification criterion is: when the erosion gully exceeds 80% of the angle between the center line of the gully and the contour line, and the slope of the slope surface perpendicular to the center line of the gully is greater than the slope of the center line of the gully, the erosion gully is classified as a valley bottom gully; when the erosion gully exceeds 80% of the angle between the center line of the gully and the contour line, and the slope of the slope surface perpendicular to the center line of the gully is less than the slope of the center line of the gully, the erosion gully is classified as a slope gully.

6. An electronic device, comprising: The method comprises the following steps of: a processor and a memory; the memory stores a computer program that can be executed by the processor; the processor executes the computer program to implement the steps of the erosion gully classification method according to any one of claims 1-4.

7. A computer readable storage medium characterized in that, The computer readable storage medium stores one or more programs that can be executed by one or more processors to implement the steps of the erosion gully classification method according to any one of claims 1-4.

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